Summary
Lack of access to projection data from patient CT scans is a major limitation for the development and validation of new CT reconstruction algorithms. However, patient projection data acquired using commercial CT scanners are stored in a proprietary format, requiring investigators to have research agreements in place with the CT manufacturer to be able to read the data correctly. These proprietary data formats for projection data, making it unlikely that investigators will be able to validate their algorithm on data from more than one manufacturer.
To meet this critical need, researchers at the Mayo Clinic, with funding from the National Institute of Biomedical Imaging and Bioengineering (EB 017185), have built a library of CT patient projection data in an open and vendor-neutral format. This format, referred to as DICOM-CT-PD, is an extended DICOM format that contains CT projection data and acquisition geometry. The de-identified patient projection data in the library were decoded with help of the manufacturer and have been converted into an open and standard format.
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title | Data Access |
Data Access
Click the Download button to save a ".tcia" manifest file to your computer, which you must open with the NBIA Data Retriever. Click the Search button to open our Data Portal, where you can browse the data collection and/or download a subset of its contents.
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Matlab DICOM-CTPD data dictionary (.txt)
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Click the Versions tab for more info about data releases.
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title | Detailed Description |
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Investigators at the Mayo Clinic, with funding from the National Institute of Biomedical Imaging and Bioengineering (EB 017185), have built a library of CT patient projection data in an open and vendor-neutral format. This format, referred to as DICOM-CT-PD (1), is an extended DICOM format that contains CT projection data and acquisition geometry. The de-identified patient projection data in the library were decoded with help of the manufacturer and have been converted into an open standardized format.
Reconstructed images, patient age and gender, and pathology annotation are also provided in these de-identified data sets. The library consists of scans from various exam types, including non-contrast head CT scans acquired for acute cognitive or motor deficit, low-dose non-contrast chest scans acquired to screen high-risk patients for pulmonary nodules, and contrast-enhanced CT scans of the abdomen acquired to look for metastatic liver lesions.
For each scan, three types of data are provided, including DICOM-CT-PD projection data, reconstructed CT images, and an Excel file. CT data are provided for both full and simulated lower dose levels, albeit reconstructed image data of the lower dose projection data are available only for the data from Siemens Healthcare, where we were able to return the lower dose data to the scanner for reconstruction with their commercial filtered-back-projection algorithm. Several instructional documents are provided to help users extract needed information from the DICOM-CT-PD files, including a dictionary file for the DICOM-CT-PD format, a DICOM-CT-PD reader, and a user manual.
This collection will comprise 150 head scans, 150 chest scans, and 150 abdomen scans. For each scan type, the 50 currently available cases are from a SOMATOM Definition Flash CT scanner (Siemens Healthcare, Forchheim, Germany). An additional 50 cases for each exam type are being prepared from a Lightspeed VCT CT scanner (GE Healthcare, Waukesha, WI) and 50 a Brilliance iCT CT scanner, also known as an iCT Elite, (Philips Healthcare, Best, Netherlands). Together, these data will greatly facilitate the development and validation of new CT reconstruction and/or denoising algorithms, including those associated with machine learning or artificial intelligence.
Acquisition protocol
All CT scans were acquired at routine dose levels for the practice at which they were obtained using standard-clinical protocols for the anatomical region of interest. Each clinical case was processed to include a second projection dataset at a simulated lower dose level. Head and abdomen cases are provided at 25% of the routine dose and chest cases are provided at 10% of the routine dose.
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title | Citations & Data Usage Policy |
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Citations & Data Usage Policy
These collections are freely available to browse, download, and use for commercial, scientific and educational purposes as outlined in the Creative Commons Attribution 3.0 Unported License. Questions may be directed to help@cancerimagingarchive.net.
Please be sure to acknowledge both this data set and TCIA in publications by including the following citations in your work:
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Data can be shared publicly; however use of one CT manufacturer’s data by other CT manufacturers is prohibited. Chen B, Duan X, Yu Z, Leng S, Yu L, McCollough CH. Data from Low Dose CT Metastases Images [Data set]. The Cancer Imaging Archive. (DOI goes here.) |
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Chen B, Duan X, Yu Z, Leng S, Yu L, McCollough CH. Technical Note: Development and validation of an open data format for CT projection data. Med Phys. 2015;42(12):6964. (doi: https://doi.org/10.1118/1.4935406.) |
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We would like to acknowledge the individuals and institutions that have provided data for this collection:
We also acknowledge the following individuals who assisted in converting their company’s proprietary data format into DICOM-CT-PD:
This work would not have been possible without the support and assistance of many additional individuals.
We have greatly valued the insightful comments and helpful suggestions from the external advisory committee and consultants from Mayo Clinic’s sub-mSv CT U01 award from the National Institute of Biomedical Imaging and Bioengineering (NIBIB) (EB 017185), the other NIBIB sub-mSv CT U01 recipients, and additional consultants, including: Norbert Pelc, Sc.D. (Stanford University), Mark Baker, M.D. (Cleveland Clinic), Kyle Meyers, Ph.D. (U.S. Food and Drug Administration), Matthew Kupinski, Ph.D. (University of Arizona), Alicia Toledano, Ph.D. (Biostatistics Consulting, LLC), Peter Edic, Ph.D. (General Electric Global Research), Ge Wang, Ph.D. (Rensselaer Polytechnic Institute), Hengyong Yu, Ph.D. (University of Massachusetts Lowell), Daniel Sodickson, M.D., Ph.D. (New York University Langone Health), Aaron Sodickson, M.D., Ph.D. (Brigham and Women’s Hospital), Ricardo Otazo, M.D. (Memorial Sloan Kettering), Webster Stayman, Ph.D. (Johns Hopkins University), Grace Gang, Ph.D. (Johns Hopkins University), Reuven Levinson (Philips Healthcare), Jeffrey Fessler, Ph.D. (University of Michigan), David Clunie, MBBS (PixelMed Publishing) From Mayo Clinic’s CT Clinical Innovation Center, located within the Department of Radiology, this work would not have been possible without the skill, perseverance, and can-do attitude of the Mayo research team, including faculty, trainees, and support staff who worked together across every aspect of this project. Sincere gratitude is expressed to each of the following individuals: Cynthia McCollough, Ph.D., Joel Fletcher, Ph.D., David Holmes, III, Ph.D., Shuai Leng, Ph.D., Lifeng Yu, Ph.D., Rickey Carter, Ph.D., Baiyu Chen, Ph.D., Xinhui Duan, Ph.D., Kyle McMillan, Ph.D., Hao Gong, Ph.D., Gregory Michalak, Ph.D., Liqiang Ren, Ph.D., Tammy Drees, Maria Shiung |
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Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: 10.1007/s10278-013-9622-7 |
Other Publications Using This Data
TCIA maintains a list of publications which leverage TCIA data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk.
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Excerpt |
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Investigators at the Mayo Clinic, with funding from the National Institute of Biomedical Imaging and Bioengineering (EB 017095 and EB 017185), have built a library of CT patient projection data in an open and vendor-neutral format. This format, referred to as DICOM-CT-PD (1), is an extended DICOM format that contains CT projection data and acquisition geometry. The de-identified patient projection data in the library were decoded with help of the manufacturer and have been converted into an open standardized format. Reconstructed images, patient age and gender, and pathology annotation are also provided for these de-identified data sets. The library consists of scans from various exam types, including non-contrast head CT scans acquired for acute cognitive or motor deficit, low-dose non-contrast chest scans acquired to screen high-risk patients for pulmonary nodules, and contrast-enhanced CT scans of the abdomen acquired to look for metastatic liver lesions. 2016 Low Dose CT Grand Challenge The 2016 Low Dose CT Grand Challenge, sponsored by the AAPM, NIBIB, and Mayo Clinic, used 30 contrast-enhanced abdominal CT patient scans, 10 for training and 20 for testing. Thirteen of the 20 testing datasets from the Grand Challenge were subsequently included in this larger collection of CT image and projection data (TCIA LDCT-and-Projection-data). Because of the frequency of requests received by Mayo and the AAPM for the complete 2016 Grand Challenge dataset, on September 21, 2021 all 30 cases were updated to use the same projection data format as used for the TCIA data library and made publicly available in a single location. Please refer to the READ ME file at that location for a mapping between the case ID numbers used in the 2016 Grand Challenge and the case ID numbers used in the TCIA library for the 13 cases that exist in both libraries. Additional information about the 2016 Low Dose CT Grand Challenge can be found on the AAPM website and in the Medical Physics paper by McCollough et al. |
Acknowledgements
This work would not have been possible without the support and efforts of many individuals and organizations.
- A complete list of acknowledgements can be found here.
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title | Versions |
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Version 1 (Current): Updated yyyy/mm/dd
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(Requires NBIA Data Retriever.)
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Matlab DICOM-CTPD data dictionary (.txt)
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